Overview

Dataset statistics

Number of variables50
Number of observations388
Missing cells0
Missing cells (%)0.0%
Duplicate rows64
Duplicate rows (%)16.5%
Total size in memory154.6 KiB
Average record size in memory408.0 B

Variable types

Numeric4
Categorical45
Boolean1

Alerts

Dataset has 64 (16.5%) duplicate rowsDuplicates
Time_saving is highly correlated with orderAgainHigh correlation
More_Offers_and_Discount is highly correlated with orderAgainHigh correlation
Meal(P1) is highly correlated with Meal(P2)High correlation
Easy_Payment_option is highly correlated with orderAgainHigh correlation
Ease_and_convenient is highly correlated with orderAgainHigh correlation
Good_Taste is highly correlated with Good_Quantity and 1 other fieldsHigh correlation
Occupation is highly correlated with Monthly_Income and 1 other fieldsHigh correlation
Monthly_Income is highly correlated with Occupation and 1 other fieldsHigh correlation
Meal(P2) is highly correlated with Meal(P1)High correlation
orderAgain is highly correlated with Time_saving and 4 other fieldsHigh correlation
More_restaurant_choices is highly correlated with orderAgainHigh correlation
Good_Quantity is highly correlated with Good_TasteHigh correlation
Marital_Status is highly correlated with Occupation and 1 other fieldsHigh correlation
Freshness is highly correlated with Good_TasteHigh correlation
Age is highly correlated with Marital_Status and 3 other fieldsHigh correlation
Marital_Status is highly correlated with Age and 1 other fieldsHigh correlation
Occupation is highly correlated with Age and 2 other fieldsHigh correlation
Monthly_Income is highly correlated with Age and 1 other fieldsHigh correlation
Educational_Qualifications is highly correlated with AgeHigh correlation
latitude is highly correlated with longitudeHigh correlation
longitude is highly correlated with latitudeHigh correlation
Meal(P1) is highly correlated with Meal(P2)High correlation
Meal(P2) is highly correlated with Meal(P1)High correlation
Perference(P1) is highly correlated with Perference(P2)High correlation
Perference(P2) is highly correlated with Perference(P1)High correlation
Ease_and_convenient is highly correlated with Time_saving and 19 other fieldsHigh correlation
Time_saving is highly correlated with Ease_and_convenient and 6 other fieldsHigh correlation
More_restaurant_choices is highly correlated with Ease_and_convenient and 7 other fieldsHigh correlation
Easy_Payment_option is highly correlated with Ease_and_convenient and 6 other fieldsHigh correlation
More_Offers_and_Discount is highly correlated with Ease_and_convenient and 6 other fieldsHigh correlation
Good_Food_quality is highly correlated with Ease_and_convenient and 8 other fieldsHigh correlation
Good_Tracking_system is highly correlated with Ease_and_convenient and 5 other fieldsHigh correlation
Self_Cooking is highly correlated with Ease_and_convenient and 3 other fieldsHigh correlation
Health_Concern is highly correlated with Self_Cooking and 4 other fieldsHigh correlation
Late_Delivery is highly correlated with Health_Concern and 5 other fieldsHigh correlation
Poor_Hygiene is highly correlated with Ease_and_convenient and 9 other fieldsHigh correlation
Bad_past_experience is highly correlated with Good_Food_quality and 10 other fieldsHigh correlation
Unavailability is highly correlated with Ease_and_convenient and 9 other fieldsHigh correlation
Unaffordable is highly correlated with Late_Delivery and 3 other fieldsHigh correlation
Long_delivery_time is highly correlated with Ease_and_convenient and 14 other fieldsHigh correlation
Delay_of_delivery_person_getting_assigned is highly correlated with Poor_Hygiene and 5 other fieldsHigh correlation
Delay_of_delivery_person_picking_up_food is highly correlated with Long_delivery_time and 2 other fieldsHigh correlation
Wrong_order_delivered is highly correlated with Ease_and_convenient and 7 other fieldsHigh correlation
Missing_item is highly correlated with Poor_Hygiene and 6 other fieldsHigh correlation
Order_placed_by_mistake is highly correlated with Wrong_order_delivered and 1 other fieldsHigh correlation
Maximum_wait_time is highly correlated with Ease_and_convenient and 3 other fieldsHigh correlation
Residence_in_busy_location is highly correlated with Ease_and_convenient and 12 other fieldsHigh correlation
Good_Road_Condition is highly correlated with Ease_and_convenient and 9 other fieldsHigh correlation
Low_quantity_low_time is highly correlated with Residence_in_busy_location and 1 other fieldsHigh correlation
Delivery_person_ability is highly correlated with Residence_in_busy_location and 2 other fieldsHigh correlation
Less_Delivery_time is highly correlated with Residence_in_busy_location and 6 other fieldsHigh correlation
High_Quality_of_package is highly correlated with Ease_and_convenient and 10 other fieldsHigh correlation
Number_of_calls is highly correlated with Less_Delivery_time and 3 other fieldsHigh correlation
Politeness is highly correlated with Ease_and_convenient and 10 other fieldsHigh correlation
Freshness is highly correlated with Ease_and_convenient and 12 other fieldsHigh correlation
Temperature is highly correlated with High_Quality_of_package and 4 other fieldsHigh correlation
Good_Taste is highly correlated with Ease_and_convenient and 10 other fieldsHigh correlation
Good_Quantity is highly correlated with Ease_and_convenient and 12 other fieldsHigh correlation
orderAgain is highly correlated with Ease_and_convenient and 1 other fieldsHigh correlation

Reproduction

Analysis started2022-03-05 18:51:02.780229
Analysis finished2022-03-05 18:51:17.831506
Duration15.05 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.61597938
Minimum21
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2022-03-05T12:51:17.949193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q123
median24
Q326
95-th percentile30
Maximum30
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.596848083
Coefficient of variation (CV)0.1054944044
Kurtosis-0.4531404
Mean24.61597938
Median Absolute Deviation (MAD)2
Skewness0.7038581918
Sum9551
Variance6.743619969
MonotonicityNot monotonic
2022-03-05T12:51:18.037955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2382
21.1%
2257
14.7%
2552
13.4%
2450
12.9%
2635
9.0%
3034
8.8%
2128
 
7.2%
2721
 
5.4%
2815
 
3.9%
2914
 
3.6%
ValueCountFrequency (%)
2128
 
7.2%
2257
14.7%
2382
21.1%
2450
12.9%
2552
13.4%
2635
9.0%
2721
 
5.4%
2815
 
3.9%
2914
 
3.6%
3034
8.8%
ValueCountFrequency (%)
3034
8.8%
2914
 
3.6%
2815
 
3.9%
2721
 
5.4%
2635
9.0%
2552
13.4%
2450
12.9%
2382
21.1%
2257
14.7%
2128
 
7.2%

Gender
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Male
222 
Female
166 

Length

Max length6
Median length4
Mean length4.855670103
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male222
57.2%
Female166
42.8%

Length

2022-03-05T12:51:18.244435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:18.320226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
male222
57.2%
female166
42.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Marital_Status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Single
280 
Married
108 

Length

Max length7
Median length6
Mean length6.278350515
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Single280
72.2%
Married108
 
27.8%

Length

2022-03-05T12:51:18.396994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:18.463853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
single280
72.2%
married108
 
27.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Occupation
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Student
207 
Employee
118 
Self Employeed
54 
House wife
 
9

Length

Max length14
Median length7
Mean length8.347938144
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStudent
2nd rowStudent
3rd rowStudent
4th rowStudent
5th rowStudent

Common Values

ValueCountFrequency (%)
Student207
53.4%
Employee118
30.4%
Self Employeed54
 
13.9%
House wife9
 
2.3%

Length

2022-03-05T12:51:18.548589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:18.627403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
student207
45.9%
employee118
26.2%
self54
 
12.0%
employeed54
 
12.0%
house9
 
2.0%
wife9
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Monthly_Income
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
No Income
187 
25001 to 50000
69 
More than 50000
62 
10001 to 25000
45 
Below Rs.10000
25 

Length

Max length15
Median length14
Mean length11.75
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Income
2nd rowBelow Rs.10000
3rd rowBelow Rs.10000
4th rowNo Income
5th rowBelow Rs.10000

Common Values

ValueCountFrequency (%)
No Income187
48.2%
25001 to 5000069
 
17.8%
More than 5000062
 
16.0%
10001 to 2500045
 
11.6%
Below Rs.1000025
 
6.4%

Length

2022-03-05T12:51:18.742071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:18.821884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
no187
19.6%
income187
19.6%
50000131
13.8%
to114
12.0%
2500169
 
7.2%
more62
 
6.5%
than62
 
6.5%
1000145
 
4.7%
2500045
 
4.7%
below25
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Educational_Qualifications
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Graduate
177 
Post Graduate
174 
Ph.D
23 
School
 
12
Uneducated
 
2

Length

Max length13
Median length8
Mean length9.953608247
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPost Graduate
2nd rowGraduate
3rd rowPost Graduate
4th rowGraduate
5th rowPost Graduate

Common Values

ValueCountFrequency (%)
Graduate177
45.6%
Post Graduate174
44.8%
Ph.D23
 
5.9%
School12
 
3.1%
Uneducated2
 
0.5%

Length

2022-03-05T12:51:18.940568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:19.015439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate351
62.5%
post174
31.0%
ph.d23
 
4.1%
school12
 
2.1%
uneducated2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Family_size
Real number (ℝ≥0)

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.280927835
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2022-03-05T12:51:19.114177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.35102494
Coefficient of variation (CV)0.4117813642
Kurtosis-0.6634980527
Mean3.280927835
Median Absolute Deviation (MAD)1
Skewness0.4013763516
Sum1273
Variance1.825268388
MonotonicityNot monotonic
2022-03-05T12:51:19.201975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3117
30.2%
2101
26.0%
463
16.2%
554
13.9%
629
 
7.5%
124
 
6.2%
ValueCountFrequency (%)
124
 
6.2%
2101
26.0%
3117
30.2%
463
16.2%
554
13.9%
629
 
7.5%
ValueCountFrequency (%)
629
 
7.5%
554
13.9%
463
16.2%
3117
30.2%
2101
26.0%
124
 
6.2%

latitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.97205799
Minimum12.8652
Maximum13.102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2022-03-05T12:51:19.307684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12.8652
5-th percentile12.8988
Q112.9369
median12.977
Q312.997025
95-th percentile13.058295
Maximum13.102
Range0.2368
Interquartile range (IQR)0.060125

Descriptive statistics

Standard deviation0.04448924863
Coefficient of variation (CV)0.003429621473
Kurtosis0.03057286742
Mean12.97205799
Median Absolute Deviation (MAD)0.0276
Skewness0.1602584132
Sum5033.1585
Variance0.001979293243
MonotonicityNot monotonic
2022-03-05T12:51:19.449307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.97736
 
9.3%
12.978316
 
4.1%
12.98514
 
3.6%
12.884512
 
3.1%
12.936911
 
2.8%
12.926111
 
2.8%
12.90489
 
2.3%
12.97069
 
2.3%
13.02068
 
2.1%
12.93438
 
2.1%
Other values (67)254
65.5%
ValueCountFrequency (%)
12.86521
 
0.3%
12.88342
 
0.5%
12.884512
3.1%
12.88934
 
1.0%
12.89885
1.3%
12.90373
 
0.8%
12.90489
2.3%
12.91052
 
0.5%
12.91192
 
0.5%
12.91494
 
1.0%
ValueCountFrequency (%)
13.1022
 
0.5%
13.08092
 
0.5%
13.07344
1.0%
13.06415
1.3%
13.06267
1.8%
13.05032
 
0.5%
13.04961
 
0.3%
13.04878
2.1%
13.02982
 
0.5%
13.02893
 
0.8%

longitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct76
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.60015954
Minimum77.4842
Maximum77.7582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2022-03-05T12:51:19.577935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum77.4842
5-th percentile77.5376
Q177.565275
median77.5921
Q377.6309
95-th percentile77.6848
Maximum77.7582
Range0.274
Interquartile range (IQR)0.065625

Descriptive statistics

Standard deviation0.05135391701
Coefficient of variation (CV)0.0006617759206
Kurtosis0.9844538259
Mean77.60015954
Median Absolute Deviation (MAD)0.03
Skewness0.801995129
Sum30108.8619
Variance0.002637224793
MonotonicityNot monotonic
2022-03-05T12:51:19.689636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.577336
 
9.3%
77.640816
 
4.1%
77.553314
 
3.6%
77.603612
 
3.1%
77.640711
 
2.8%
77.622111
 
2.8%
77.68219
 
2.3%
77.65299
 
2.3%
77.64798
 
2.1%
77.60448
 
2.1%
Other values (66)254
65.5%
ValueCountFrequency (%)
77.48422
 
0.5%
77.49042
 
0.5%
77.49212
 
0.5%
77.49411
 
0.3%
77.49921
 
0.3%
77.51281
 
0.3%
77.51351
 
0.3%
77.5241
 
0.3%
77.52847
1.8%
77.53321
 
0.3%
ValueCountFrequency (%)
77.75824
1.0%
77.758
2.1%
77.71322
 
0.5%
77.70814
1.0%
77.68485
1.3%
77.68219
2.3%
77.68043
 
0.8%
77.67134
1.0%
77.65936
1.5%
77.65299
2.3%

Meal(P1)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Snacks
124 
Lunch
120 
Dinner
91 
Breakfast
53 

Length

Max length9
Median length6
Mean length6.100515464
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBreakfast
2nd rowSnacks
3rd rowLunch
4th rowSnacks
5th rowLunch

Common Values

ValueCountFrequency (%)
Snacks124
32.0%
Lunch120
30.9%
Dinner91
23.5%
Breakfast53
13.7%

Length

2022-03-05T12:51:19.823285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:19.908078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
snacks124
32.0%
lunch120
30.9%
dinner91
23.5%
breakfast53
13.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Meal(P2)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Dinner
312 
Snacks
48 
Lunch
 
28

Length

Max length6
Median length6
Mean length5.927835052
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLunch
2nd rowDinner
3rd rowSnacks
4th rowDinner
5th rowDinner

Common Values

ValueCountFrequency (%)
Dinner312
80.4%
Snacks48
 
12.4%
Lunch28
 
7.2%

Length

2022-03-05T12:51:20.016761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:20.089592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
dinner312
80.4%
snacks48
 
12.4%
lunch28
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Perference(P1)
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Non Veg foods (Lunch / Dinner)
315 
Veg foods (Breakfast / Lunch / Dinner)
69 
Sweets
 
3
Bakery items (snacks)
 
1

Length

Max length38
Median length30
Mean length31.21391753
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowNon Veg foods (Lunch / Dinner)
2nd rowNon Veg foods (Lunch / Dinner)
3rd rowNon Veg foods (Lunch / Dinner)
4th rowVeg foods (Breakfast / Lunch / Dinner)
5th rowNon Veg foods (Lunch / Dinner)

Common Values

ValueCountFrequency (%)
Non Veg foods (Lunch / Dinner)315
81.2%
Veg foods (Breakfast / Lunch / Dinner)69
 
17.8%
Sweets3
 
0.8%
Bakery items (snacks)1
 
0.3%

Length

2022-03-05T12:51:20.185336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:20.263128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
453
19.0%
veg384
16.1%
foods384
16.1%
lunch384
16.1%
dinner384
16.1%
non315
13.2%
breakfast69
 
2.9%
sweets3
 
0.1%
bakery1
 
< 0.1%
items1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Perference(P2)
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Veg foods (Breakfast / Lunch / Dinner)
294 
Sweets
46 
Ice cream / Cool drinks
35 
Bakery items (snacks)
 
13

Length

Max length38
Median length38
Mean length32.28350515
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBakery items (snacks)
2nd rowVeg foods (Breakfast / Lunch / Dinner)
3rd rowIce cream / Cool drinks
4th rowBakery items (snacks)
5th rowVeg foods (Breakfast / Lunch / Dinner)

Common Values

ValueCountFrequency (%)
Veg foods (Breakfast / Lunch / Dinner)294
75.8%
Sweets46
 
11.9%
Ice cream / Cool drinks35
 
9.0%
Bakery items (snacks)13
 
3.4%

Length

2022-03-05T12:51:20.374830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:20.447639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
623
26.9%
veg294
12.7%
foods294
12.7%
breakfast294
12.7%
lunch294
12.7%
dinner294
12.7%
sweets46
 
2.0%
ice35
 
1.5%
cream35
 
1.5%
cool35
 
1.5%
Other values (4)74
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Ease_and_convenient
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
235 
Strongly agree
67 
Disagree
58 
Neutral
 
20
Strongly disagree
 
8

Length

Max length17
Median length5
Mean length7.353092784
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowStrongly agree
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree235
60.6%
Strongly agree67
 
17.3%
Disagree58
 
14.9%
Neutral20
 
5.2%
Strongly disagree8
 
2.1%

Length

2022-03-05T12:51:20.563300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:20.639134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree302
65.2%
strongly75
 
16.2%
disagree66
 
14.3%
neutral20
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Time_saving
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
143 
Strongly agree
115 
Disagree
66 
Neutral
55 
Strongly disagree
 
9

Length

Max length17
Median length7
Mean length8.739690722
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowStrongly agree
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree143
36.9%
Strongly agree115
29.6%
Disagree66
17.0%
Neutral55
 
14.2%
Strongly disagree9
 
2.3%

Length

2022-03-05T12:51:20.763791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:20.936348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree258
50.4%
strongly124
24.2%
disagree75
 
14.6%
neutral55
 
10.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

More_restaurant_choices
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
169 
Strongly agree
104 
Neutral
55 
Disagree
45 
Strongly disagree
 
15

Length

Max length17
Median length7
Mean length8.507731959
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowStrongly agree
4th rowStrongly agree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree169
43.6%
Strongly agree104
26.8%
Neutral55
 
14.2%
Disagree45
 
11.6%
Strongly disagree15
 
3.9%

Length

2022-03-05T12:51:21.061965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:21.136765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree273
53.8%
strongly119
23.5%
disagree60
 
11.8%
neutral55
 
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Easy_Payment_option
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
143 
Strongly agree
93 
Neutral
76 
Disagree
52 
Strongly disagree
24 

Length

Max length17
Median length7
Mean length8.693298969
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowNeutral
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree143
36.9%
Strongly agree93
24.0%
Neutral76
19.6%
Disagree52
 
13.4%
Strongly disagree24
 
6.2%

Length

2022-03-05T12:51:21.260434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:21.334237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree236
46.7%
strongly117
23.2%
neutral76
 
15.0%
disagree76
 
15.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

More_Offers_and_Discount
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
132 
Strongly agree
94 
Neutral
76 
Disagree
63 
Strongly disagree
23 

Length

Max length17
Median length7
Mean length8.770618557
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowNeutral
4th rowStrongly agree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree132
34.0%
Strongly agree94
24.2%
Neutral76
19.6%
Disagree63
16.2%
Strongly disagree23
 
5.9%

Length

2022-03-05T12:51:21.466908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:21.551655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree226
44.8%
strongly117
23.2%
disagree86
 
17.0%
neutral76
 
15.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Good_Food_quality
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
141 
Neutral
112 
Strongly agree
68 
Disagree
49 
Strongly disagree
18 

Length

Max length17
Median length7
Mean length8.090206186
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowNeutral
3rd rowDisagree
4th rowAgree
5th rowNeutral

Common Values

ValueCountFrequency (%)
Agree141
36.3%
Neutral112
28.9%
Strongly agree68
17.5%
Disagree49
 
12.6%
Strongly disagree18
 
4.6%

Length

2022-03-05T12:51:21.672332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:21.743143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree209
44.1%
neutral112
23.6%
strongly86
18.1%
disagree67
 
14.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Good_Tracking_system
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
161 
Strongly agree
111 
Neutral
68 
Disagree
27 
Strongly disagree
21 

Length

Max length17
Median length7
Mean length8.783505155
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowAgree
3rd rowNeutral
4th rowAgree
5th rowNeutral

Common Values

ValueCountFrequency (%)
Agree161
41.5%
Strongly agree111
28.6%
Neutral68
17.5%
Disagree27
 
7.0%
Strongly disagree21
 
5.4%

Length

2022-03-05T12:51:21.863820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:21.948594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree272
52.3%
strongly132
25.4%
neutral68
 
13.1%
disagree48
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Self_Cooking
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
166 
Disagree
140 
Neutral
48 
Strongly agree
19 
Strongly disagree
 
15

Length

Max length17
Median length7
Mean length7.234536082
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowDisagree
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree166
42.8%
Disagree140
36.1%
Neutral48
 
12.4%
Strongly agree19
 
4.9%
Strongly disagree15
 
3.9%

Length

2022-03-05T12:51:22.080242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:22.160028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree185
43.8%
disagree155
36.7%
neutral48
 
11.4%
strongly34
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Health_Concern
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
121 
Disagree
119 
Neutral
68 
Strongly agree
67 
Strongly disagree
13 

Length

Max length17
Median length8
Mean length8.226804124
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowNeutral
4th rowStrongly agree
5th rowStrongly agree

Common Values

ValueCountFrequency (%)
Agree121
31.2%
Disagree119
30.7%
Neutral68
17.5%
Strongly agree67
17.3%
Strongly disagree13
 
3.4%

Length

2022-03-05T12:51:22.288685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:22.372459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree188
40.2%
disagree132
28.2%
strongly80
17.1%
neutral68
 
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Late_Delivery
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
163 
Neutral
87 
Disagree
74 
Strongly disagree
33 
Strongly agree
31 

Length

Max length17
Median length7
Mean length7.760309278
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowAgree
3rd rowNeutral
4th rowNeutral
5th rowStrongly agree

Common Values

ValueCountFrequency (%)
Agree163
42.0%
Neutral87
22.4%
Disagree74
19.1%
Strongly disagree33
 
8.5%
Strongly agree31
 
8.0%

Length

2022-03-05T12:51:22.496129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:22.577910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree194
42.9%
disagree107
23.7%
neutral87
19.2%
strongly64
 
14.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Poor_Hygiene
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Disagree
112 
Agree
110 
Neutral
88 
Strongly agree
52 
Strongly disagree
26 

Length

Max length17
Median length7
Mean length8.329896907
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowAgree
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Disagree112
28.9%
Agree110
28.4%
Neutral88
22.7%
Strongly agree52
13.4%
Strongly disagree26
 
6.7%

Length

2022-03-05T12:51:22.726541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:22.804331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree162
34.8%
disagree138
29.6%
neutral88
18.9%
strongly78
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Bad_past_experience
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Disagree
143 
Agree
99 
Neutral
85 
Strongly agree
31 
Strongly disagree
30 

Length

Max length17
Median length8
Mean length8.190721649
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowAgree
4th rowDisagree
5th rowStrongly agree

Common Values

ValueCountFrequency (%)
Disagree143
36.9%
Agree99
25.5%
Neutral85
21.9%
Strongly agree31
 
8.0%
Strongly disagree30
 
7.7%

Length

2022-03-05T12:51:22.924982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:23.004769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
disagree173
38.5%
agree130
29.0%
neutral85
18.9%
strongly61
 
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Unavailability
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Disagree
126 
Agree
95 
Neutral
73 
Strongly disagree
63 
Strongly agree
31 

Length

Max length17
Median length8
Mean length9.018041237
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowAgree
4th rowDisagree
5th rowAgree

Common Values

ValueCountFrequency (%)
Disagree126
32.5%
Agree95
24.5%
Neutral73
18.8%
Strongly disagree63
16.2%
Strongly agree31
 
8.0%

Length

2022-03-05T12:51:23.141421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:23.226176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
disagree189
39.2%
agree126
26.1%
strongly94
19.5%
neutral73
 
15.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Unaffordable
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Disagree
153 
Strongly disagree
82 
Agree
63 
Neutral
62 
Strongly agree
28 

Length

Max length17
Median length8
Mean length9.68814433
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly agree
3rd rowAgree
4th rowNeutral
5th rowDisagree

Common Values

ValueCountFrequency (%)
Disagree153
39.4%
Strongly disagree82
21.1%
Agree63
16.2%
Neutral62
16.0%
Strongly agree28
 
7.2%

Length

2022-03-05T12:51:23.354832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:23.436615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
disagree235
47.2%
strongly110
22.1%
agree91
 
18.3%
neutral62
 
12.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Long_delivery_time
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
185 
Disagree
108 
Neutral
46 
Strongly agree
42 
Strongly disagree
 
7

Length

Max length17
Median length7
Mean length7.262886598
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly agree
3rd rowAgree
4th rowAgree
5th rowStrongly agree

Common Values

ValueCountFrequency (%)
Agree185
47.7%
Disagree108
27.8%
Neutral46
 
11.9%
Strongly agree42
 
10.8%
Strongly disagree7
 
1.8%

Length

2022-03-05T12:51:23.557291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:23.628101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree227
51.9%
disagree115
26.3%
strongly49
 
11.2%
neutral46
 
10.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
142 
Neutral
104 
Disagree
68 
Strongly agree
56 
Strongly disagree
18 

Length

Max length17
Median length7
Mean length7.917525773
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly agree
3rd rowAgree
4th rowAgree
5th rowStrongly agree

Common Values

ValueCountFrequency (%)
Agree142
36.6%
Neutral104
26.8%
Disagree68
17.5%
Strongly agree56
 
14.4%
Strongly disagree18
 
4.6%

Length

2022-03-05T12:51:23.752769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:23.827567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree198
42.9%
neutral104
22.5%
disagree86
18.6%
strongly74
 
16.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Delay_of_delivery_person_picking_up_food
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
189 
Neutral
78 
Disagree
61 
Strongly agree
44 
Strongly disagree
 
16

Length

Max length17
Median length7
Mean length7.389175258
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly agree
3rd rowAgree
4th rowAgree
5th rowNeutral

Common Values

ValueCountFrequency (%)
Agree189
48.7%
Neutral78
20.1%
Disagree61
 
15.7%
Strongly agree44
 
11.3%
Strongly disagree16
 
4.1%

Length

2022-03-05T12:51:24.059946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:24.137738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree233
52.0%
neutral78
 
17.4%
disagree77
 
17.2%
strongly60
 
13.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Wrong_order_delivered
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Disagree
129 
Agree
91 
Neutral
65 
Strongly agree
53 
Strongly disagree
50 

Length

Max length17
Median length8
Mean length9.108247423
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly agree
3rd rowStrongly agree
4th rowDisagree
5th rowNeutral

Common Values

ValueCountFrequency (%)
Disagree129
33.2%
Agree91
23.5%
Neutral65
16.8%
Strongly agree53
13.7%
Strongly disagree50
 
12.9%

Length

2022-03-05T12:51:24.264431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:24.343189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
disagree179
36.5%
agree144
29.3%
strongly103
21.0%
neutral65
 
13.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Missing_item
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Disagree
138 
Agree
74 
Strongly agree
66 
Neutral
62 
Strongly disagree
48 

Length

Max length17
Median length8
Mean length9.402061856
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly agree
3rd rowAgree
4th rowDisagree
5th rowNeutral

Common Values

ValueCountFrequency (%)
Disagree138
35.6%
Agree74
19.1%
Strongly agree66
17.0%
Neutral62
16.0%
Strongly disagree48
 
12.4%

Length

2022-03-05T12:51:24.477829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:24.557615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
disagree186
37.1%
agree140
27.9%
strongly114
22.7%
neutral62
 
12.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Order_placed_by_mistake
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Disagree
126 
Strongly disagree
87 
Agree
69 
Neutral
65 
Strongly agree
41 

Length

Max length17
Median length8
Mean length9.951030928
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly agree
3rd rowNeutral
4th rowNeutral
5th rowDisagree

Common Values

ValueCountFrequency (%)
Disagree126
32.5%
Strongly disagree87
22.4%
Agree69
17.8%
Neutral65
16.8%
Strongly agree41
 
10.6%

Length

2022-03-05T12:51:24.687268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:24.769050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
disagree213
41.3%
strongly128
24.8%
agree110
21.3%
neutral65
 
12.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Yes
301 
No
48 
Maybe
39 

Length

Max length5
Median length3
Mean length3.077319588
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes301
77.6%
No48
 
12.4%
Maybe39
 
10.1%

Length

2022-03-05T12:51:24.895724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:24.972517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
yes301
77.6%
no48
 
12.4%
maybe39
 
10.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Order_Time
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Anytime (Mon-Sun)
269 
Weekend (Sat & Sun)
102 
Weekdays (Mon-Fri)
 
17

Length

Max length19
Median length17
Mean length17.56958763
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeekend (Sat & Sun)
2nd rowAnytime (Mon-Sun)
3rd rowAnytime (Mon-Sun)
4th rowAnytime (Mon-Sun)
5th rowWeekend (Sat & Sun)

Common Values

ValueCountFrequency (%)
Anytime (Mon-Sun)269
69.3%
Weekend (Sat & Sun)102
 
26.3%
Weekdays (Mon-Fri)17
 
4.4%

Length

2022-03-05T12:51:25.072250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:25.156026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
anytime269
27.4%
mon-sun269
27.4%
weekend102
 
10.4%
sat102
 
10.4%
102
 
10.4%
sun102
 
10.4%
weekdays17
 
1.7%
mon-fri17
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Maximum_wait_time
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
45 minutes
154 
30 minutes
139 
15 minutes
40 
60 minutes
34 
More than 60 minutes
21 

Length

Max length20
Median length10
Mean length10.54123711
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30 minutes
2nd row30 minutes
3rd row45 minutes
4th row30 minutes
5th row30 minutes

Common Values

ValueCountFrequency (%)
45 minutes154
39.7%
30 minutes139
35.8%
15 minutes40
 
10.3%
60 minutes34
 
8.8%
More than 60 minutes21
 
5.4%

Length

2022-03-05T12:51:25.262741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:25.339535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
minutes388
47.4%
45154
 
18.8%
30139
 
17.0%
6055
 
6.7%
1540
 
4.9%
more21
 
2.6%
than21
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Residence_in_busy_location
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
266 
Neutral
50 
Disagree
35 
Strongly Agree
28 
Strongly disagree
 
9

Length

Max length17
Median length5
Mean length6.456185567
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly Agree
3rd rowAgree
4th rowDisagree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree266
68.6%
Neutral50
 
12.9%
Disagree35
 
9.0%
Strongly Agree28
 
7.2%
Strongly disagree9
 
2.3%

Length

2022-03-05T12:51:25.466197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:25.541994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree294
69.2%
neutral50
 
11.8%
disagree44
 
10.4%
strongly37
 
8.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Good_Road_Condition
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
177 
Strongly Agree
87 
Neutral
76 
Disagree
41 
Strongly disagree
 
7

Length

Max length17
Median length7
Mean length7.943298969
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowDisagree
3rd rowNeutral
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree177
45.6%
Strongly Agree87
22.4%
Neutral76
19.6%
Disagree41
 
10.6%
Strongly disagree7
 
1.8%

Length

2022-03-05T12:51:25.668682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:25.744452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree264
54.8%
strongly94
 
19.5%
neutral76
 
15.8%
disagree48
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Low_quantity_low_time
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
169 
Neutral
97 
Disagree
61 
Strongly Agree
34 
Strongly disagree
27 

Length

Max length17
Median length7
Mean length7.595360825
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowStrongly disagree
3rd rowNeutral
4th rowNeutral
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree169
43.6%
Neutral97
25.0%
Disagree61
 
15.7%
Strongly Agree34
 
8.8%
Strongly disagree27
 
7.0%

Length

2022-03-05T12:51:25.866128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:25.943920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree203
45.2%
neutral97
21.6%
disagree88
19.6%
strongly61
 
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Delivery_person_ability
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Agree
163 
Strongly Agree
87 
Neutral
80 
Disagree
52 
Strongly disagree
 
6

Length

Max length17
Median length7
Mean length8.018041237
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowAgree
3rd rowAgree
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree163
42.0%
Strongly Agree87
22.4%
Neutral80
20.6%
Disagree52
 
13.4%
Strongly disagree6
 
1.5%

Length

2022-03-05T12:51:26.069583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:26.145380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
agree250
52.0%
strongly93
 
19.3%
neutral80
 
16.6%
disagree58
 
12.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Yes
286 
No
53 
Maybe
49 

Length

Max length5
Median length3
Mean length3.115979381
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes286
73.7%
No53
 
13.7%
Maybe49
 
12.6%

Length

2022-03-05T12:51:26.266057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:26.341855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
yes286
73.7%
no53
 
13.7%
maybe49
 
12.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Less_Delivery_time
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Important
248 
Very Important
56 
Moderately Important
45 
Slightly Important
34 
Unimportant
 
5

Length

Max length20
Median length9
Mean length11.81185567
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowImportant
4th rowVery Important
5th rowImportant

Common Values

ValueCountFrequency (%)
Important248
63.9%
Very Important56
 
14.4%
Moderately Important45
 
11.6%
Slightly Important34
 
8.8%
Unimportant5
 
1.3%

Length

2022-03-05T12:51:26.442585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:26.515391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important383
73.2%
very56
 
10.7%
moderately45
 
8.6%
slightly34
 
6.5%
unimportant5
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

High_Quality_of_package
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Important
152 
Very Important
126 
Moderately Important
77 
Slightly Important
27 
Unimportant
 
6

Length

Max length20
Median length14
Mean length13.46391753
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowVery Important
4th rowImportant
5th rowImportant

Common Values

ValueCountFrequency (%)
Important152
39.2%
Very Important126
32.5%
Moderately Important77
19.8%
Slightly Important27
 
7.0%
Unimportant6
 
1.5%

Length

2022-03-05T12:51:26.644047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:26.727821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important382
61.8%
very126
 
20.4%
moderately77
 
12.5%
slightly27
 
4.4%
unimportant6
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number_of_calls
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Important
133 
Very Important
94 
Moderately Important
88 
Slightly Important
54 
Unimportant
19 

Length

Max length20
Median length14
Mean length14.05670103
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowModerately Important
4th rowModerately Important
5th rowModerately Important

Common Values

ValueCountFrequency (%)
Important133
34.3%
Very Important94
24.2%
Moderately Important88
22.7%
Slightly Important54
13.9%
Unimportant19
 
4.9%

Length

2022-03-05T12:51:26.857476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:26.945240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important369
59.1%
very94
 
15.1%
moderately88
 
14.1%
slightly54
 
8.7%
unimportant19
 
3.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Politeness
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Important
155 
Very Important
119 
Moderately Important
63 
Slightly Important
45 
Unimportant
 
6

Length

Max length20
Median length14
Mean length13.3943299
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowVery Important
4th rowVery Important
5th rowImportant

Common Values

ValueCountFrequency (%)
Important155
39.9%
Very Important119
30.7%
Moderately Important63
16.2%
Slightly Important45
 
11.6%
Unimportant6
 
1.5%

Length

2022-03-05T12:51:27.170664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:27.250450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important382
62.1%
very119
 
19.3%
moderately63
 
10.2%
slightly45
 
7.3%
unimportant6
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Freshness
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Very Important
137 
Important
128 
Slightly Important
60 
Moderately Important
58 
Unimportant
 
5

Length

Max length20
Median length14
Mean length13.82731959
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowVery Important
4th rowVery Important
5th rowImportant

Common Values

ValueCountFrequency (%)
Very Important137
35.3%
Important128
33.0%
Slightly Important60
15.5%
Moderately Important58
14.9%
Unimportant5
 
1.3%

Length

2022-03-05T12:51:27.374120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:27.451915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important383
59.6%
very137
 
21.3%
slightly60
 
9.3%
moderately58
 
9.0%
unimportant5
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Temperature
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Important
148 
Very Important
126 
Moderately Important
88 
Slightly Important
24 
Unimportant
 
2

Length

Max length20
Median length14
Mean length13.68556701
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowImportant
4th rowVery Important
5th rowImportant

Common Values

ValueCountFrequency (%)
Important148
38.1%
Very Important126
32.5%
Moderately Important88
22.7%
Slightly Important24
 
6.2%
Unimportant2
 
0.5%

Length

2022-03-05T12:51:27.587522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:27.660444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important386
61.7%
very126
 
20.1%
moderately88
 
14.1%
slightly24
 
3.8%
unimportant2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Good_Taste
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Very Important
178 
Important
154 
Moderately Important
39 
Slightly Important
 
11
Unimportant
 
6

Length

Max length20
Median length14
Mean length12.68556701
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowVery Important
4th rowVery Important
5th rowVery Important

Common Values

ValueCountFrequency (%)
Very Important178
45.9%
Important154
39.7%
Moderately Important39
 
10.1%
Slightly Important11
 
2.8%
Unimportant6
 
1.5%

Length

2022-03-05T12:51:27.781127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:27.854920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important382
62.0%
very178
28.9%
moderately39
 
6.3%
slightly11
 
1.8%
unimportant6
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Good_Quantity
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Very Important
174 
Important
153 
Moderately Important
33 
Slightly Important
25 
Unimportant
 
3

Length

Max length20
Median length14
Mean length12.77319588
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Important
2nd rowVery Important
3rd rowModerately Important
4th rowImportant
5th rowVery Important

Common Values

ValueCountFrequency (%)
Very Important174
44.8%
Important153
39.4%
Moderately Important33
 
8.5%
Slightly Important25
 
6.4%
Unimportant3
 
0.8%

Length

2022-03-05T12:51:27.981554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T12:51:28.067325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
important385
62.1%
very174
28.1%
moderately33
 
5.3%
slightly25
 
4.0%
unimportant3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

orderAgain
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
True
301 
False
87 
ValueCountFrequency (%)
True301
77.6%
False87
 
22.4%
2022-03-05T12:51:28.154093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2022-03-05T12:51:14.168449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:12.824044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.273867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.723638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:14.276160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:12.938748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.384546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.829377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:14.391851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.049442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.495250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.946044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:14.502555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.161144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:13.607949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-05T12:51:14.052790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-03-05T12:51:28.219948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-05T12:51:28.356588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-05T12:51:28.492217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-05T12:51:28.681708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-05T12:51:29.337960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-05T12:51:14.812725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-05T12:51:17.494443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeGenderMarital_StatusOccupationMonthly_IncomeEducational_QualificationsFamily_sizelatitudelongitudeMeal(P1)Meal(P2)Perference(P1)Perference(P2)Ease_and_convenientTime_savingMore_restaurant_choicesEasy_Payment_optionMore_Offers_and_DiscountGood_Food_qualityGood_Tracking_systemSelf_CookingHealth_ConcernLate_DeliveryPoor_HygieneBad_past_experienceUnavailabilityUnaffordableLong_delivery_timeDelay_of_delivery_person_getting_assignedDelay_of_delivery_person_picking_up_foodWrong_order_deliveredMissing_itemOrder_placed_by_mistakeInfluence_of_timeOrder_TimeMaximum_wait_timeResidence_in_busy_locationGood_Road_ConditionLow_quantity_low_timeDelivery_person_abilityInfluence_of_ratingLess_Delivery_timeHigh_Quality_of_packageNumber_of_callsPolitenessFreshnessTemperatureGood_TasteGood_QuantityorderAgain
023.0FemaleSingleStudentNo IncomePost Graduate412.976677.5993BreakfastLunchNon Veg foods (Lunch / Dinner)Bakery items (snacks)NeutralNeutralNeutralNeutralNeutralNeutralNeutralNeutralNeutralNeutralNeutralNeutralNeutralNeutralAgreeAgreeAgreeAgreeAgreeAgreeYesWeekend (Sat & Sun)30 minutesAgreeNeutralNeutralNeutralYesModerately ImportantModerately ImportantModerately ImportantModerately ImportantModerately ImportantModerately ImportantModerately ImportantModerately ImportantYes
124.0FemaleSingleStudentBelow Rs.10000Graduate312.977077.5773SnacksDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)Strongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeNeutralAgreeStrongly agreeStrongly agreeAgreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeYesAnytime (Mon-Sun)30 minutesStrongly AgreeDisagreeStrongly disagreeAgreeYesVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantYes
222.0MaleSingleStudentBelow Rs.10000Post Graduate312.955177.6593LunchSnacksNon Veg foods (Lunch / Dinner)Ice cream / Cool drinksStrongly agreeStrongly agreeStrongly agreeNeutralNeutralDisagreeNeutralDisagreeNeutralNeutralAgreeAgreeAgreeAgreeAgreeAgreeAgreeStrongly agreeAgreeNeutralYesAnytime (Mon-Sun)45 minutesAgreeNeutralNeutralAgreeYesImportantVery ImportantModerately ImportantVery ImportantVery ImportantImportantVery ImportantModerately ImportantYes
322.0FemaleSingleStudentNo IncomeGraduate612.947377.5616SnacksDinnerVeg foods (Breakfast / Lunch / Dinner)Bakery items (snacks)AgreeAgreeStrongly agreeAgreeStrongly agreeAgreeAgreeAgreeStrongly agreeNeutralAgreeDisagreeDisagreeNeutralAgreeAgreeAgreeDisagreeDisagreeNeutralYesAnytime (Mon-Sun)30 minutesDisagreeAgreeNeutralAgreeYesVery ImportantImportantModerately ImportantVery ImportantVery ImportantVery ImportantVery ImportantImportantYes
422.0MaleSingleStudentBelow Rs.10000Post Graduate412.985077.5533LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeAgreeAgreeAgreeNeutralNeutralAgreeStrongly agreeStrongly agreeAgreeStrongly agreeAgreeDisagreeStrongly agreeStrongly agreeNeutralNeutralNeutralDisagreeYesWeekend (Sat & Sun)30 minutesAgreeAgreeAgreeAgreeYesImportantImportantModerately ImportantImportantImportantImportantVery ImportantVery ImportantYes
527.0FemaleMarriedEmployeeMore than 50000Post Graduate212.929977.6848LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeNeutralAgreeAgreeNeutralAgreeDisagreeNeutralAgreeAgreeAgreeAgreeAgreeStrongly agreeStrongly agreeStrongly agreeAgreeStrongly agreeStrongly agreeYesAnytime (Mon-Sun)45 minutesNeutralNeutralNeutralNeutralYesImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantYes
622.0MaleSingleStudentNo IncomeGraduate312.977077.5773LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)Strongly agreeStrongly agreeAgreeStrongly agreeAgreeNeutralAgreeDisagreeAgreeDisagreeAgreeDisagreeStrongly agreeStrongly disagreeNeutralDisagreeDisagreeStrongly agreeStrongly agreeStrongly agreeNoAnytime (Mon-Sun)45 minutesAgreeDisagreeAgreeStrongly AgreeYesVery ImportantVery ImportantUnimportantImportantVery ImportantVery ImportantVery ImportantVery ImportantYes
724.0FemaleSingleStudentNo IncomePost Graduate312.982877.6131DinnerDinnerNon Veg foods (Lunch / Dinner)Ice cream / Cool drinksStrongly agreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeNeutralNeutralNeutralNeutralAgreeAgreeAgreeNeutralAgreeAgreeYesWeekend (Sat & Sun)45 minutesAgreeAgreeNeutralDisagreeMaybeModerately ImportantImportantModerately ImportantSlightly ImportantImportantModerately ImportantVery ImportantVery ImportantYes
823.0FemaleSingleStudentNo IncomePost Graduate212.976677.5993DinnerDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeStrongly agreeAgreeAgreeNeutralAgreeAgreeAgreeNeutralAgreeNeutralNeutralDisagreeAgreeAgreeAgreeAgreeAgreeStrongly agreeYesAnytime (Mon-Sun)30 minutesDisagreeNeutralAgreeAgreeMaybeImportantImportantImportantImportantImportantImportantImportantImportantYes
923.0FemaleSingleStudentNo IncomePost Graduate412.985477.7081LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)NeutralNeutralNeutralNeutralNeutralNeutralNeutralAgreeAgreeNeutralNeutralNeutralNeutralNeutralAgreeAgreeAgreeNeutralAgreeAgreeYesWeekend (Sat & Sun)45 minutesAgreeAgreeDisagreeDisagreeYesModerately ImportantVery ImportantModerately ImportantModerately ImportantModerately ImportantModerately ImportantVery ImportantVery ImportantYes

Last rows

AgeGenderMarital_StatusOccupationMonthly_IncomeEducational_QualificationsFamily_sizelatitudelongitudeMeal(P1)Meal(P2)Perference(P1)Perference(P2)Ease_and_convenientTime_savingMore_restaurant_choicesEasy_Payment_optionMore_Offers_and_DiscountGood_Food_qualityGood_Tracking_systemSelf_CookingHealth_ConcernLate_DeliveryPoor_HygieneBad_past_experienceUnavailabilityUnaffordableLong_delivery_timeDelay_of_delivery_person_getting_assignedDelay_of_delivery_person_picking_up_foodWrong_order_deliveredMissing_itemOrder_placed_by_mistakeInfluence_of_timeOrder_TimeMaximum_wait_timeResidence_in_busy_locationGood_Road_ConditionLow_quantity_low_timeDelivery_person_abilityInfluence_of_ratingLess_Delivery_timeHigh_Quality_of_packageNumber_of_callsPolitenessFreshnessTemperatureGood_TasteGood_QuantityorderAgain
37823.0FemaleSingleEmployee25001 to 50000Post Graduate212.984777.5491DinnerDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeNeutralAgreeAgreeStrongly agreeAgreeStrongly agreeDisagreeDisagreeStrongly disagreeDisagreeStrongly disagreeStrongly disagreeDisagreeAgreeAgreeNeutralAgreeAgreeStrongly agreeYesAnytime (Mon-Sun)30 minutesAgreeStrongly AgreeAgreeAgreeYesImportantModerately ImportantImportantImportantImportantImportantVery ImportantVery ImportantYes
37922.0MaleSingleStudentBelow Rs.10000Post Graduate412.985077.5533LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeAgreeAgreeAgreeNeutralNeutralAgreeStrongly agreeStrongly agreeAgreeStrongly agreeAgreeDisagreeStrongly agreeStrongly agreeNeutralNeutralNeutralDisagreeYesWeekend (Sat & Sun)30 minutesAgreeAgreeAgreeAgreeYesImportantImportantModerately ImportantImportantImportantImportantVery ImportantVery ImportantYes
38027.0FemaleMarriedEmployeeMore than 50000Post Graduate212.929977.6848LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeNeutralAgreeAgreeNeutralAgreeDisagreeNeutralAgreeAgreeAgreeAgreeAgreeStrongly agreeStrongly agreeStrongly agreeAgreeStrongly agreeStrongly agreeYesAnytime (Mon-Sun)45 minutesNeutralNeutralNeutralNeutralYesImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantYes
38122.0MaleSingleStudentNo IncomeGraduate312.977077.5773LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)Strongly agreeStrongly agreeAgreeStrongly agreeAgreeNeutralAgreeDisagreeAgreeDisagreeAgreeDisagreeStrongly agreeStrongly disagreeNeutralDisagreeDisagreeStrongly agreeStrongly agreeStrongly agreeNoAnytime (Mon-Sun)45 minutesAgreeDisagreeAgreeStrongly AgreeYesVery ImportantVery ImportantUnimportantImportantVery ImportantVery ImportantVery ImportantVery ImportantYes
38224.0FemaleSingleStudentNo IncomePost Graduate312.982877.6131DinnerDinnerNon Veg foods (Lunch / Dinner)Ice cream / Cool drinksStrongly agreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeNeutralNeutralNeutralNeutralAgreeAgreeAgreeNeutralAgreeAgreeYesWeekend (Sat & Sun)45 minutesAgreeAgreeNeutralDisagreeMaybeModerately ImportantImportantModerately ImportantSlightly ImportantImportantModerately ImportantVery ImportantVery ImportantYes
38323.0FemaleSingleStudentNo IncomePost Graduate212.976677.5993DinnerDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeStrongly agreeAgreeAgreeNeutralAgreeAgreeAgreeNeutralAgreeNeutralNeutralDisagreeAgreeAgreeAgreeAgreeAgreeStrongly agreeYesAnytime (Mon-Sun)30 minutesDisagreeNeutralAgreeAgreeMaybeImportantImportantImportantImportantImportantImportantImportantImportantYes
38423.0FemaleSingleStudentNo IncomePost Graduate412.985477.7081LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)NeutralNeutralNeutralNeutralNeutralNeutralNeutralAgreeAgreeNeutralNeutralNeutralNeutralNeutralAgreeAgreeAgreeNeutralAgreeAgreeYesWeekend (Sat & Sun)45 minutesAgreeAgreeDisagreeDisagreeYesModerately ImportantVery ImportantModerately ImportantModerately ImportantModerately ImportantModerately ImportantVery ImportantVery ImportantYes
38522.0FemaleSingleStudentNo IncomePost Graduate512.985077.5533SnacksDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeAgreeAgreeAgreeNeutralAgreeAgreeStrongly agreeAgreeStrongly agreeAgreeAgreeAgreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeYesAnytime (Mon-Sun)45 minutesAgreeStrongly AgreeNeutralAgreeYesImportantVery ImportantImportantImportantVery ImportantVery ImportantVery ImportantVery ImportantYes
38623.0MaleSingleStudentBelow Rs.10000Post Graduate212.977077.5773LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)Strongly agreeStrongly agreeStrongly agreeStrongly agreeAgreeAgreeStrongly agreeAgreeStrongly agreeDisagreeAgreeStrongly agreeStrongly agreeNeutralStrongly agreeStrongly agreeAgreeStrongly agreeStrongly agreeDisagreeMaybeWeekend (Sat & Sun)45 minutesStrongly AgreeAgreeDisagreeStrongly AgreeYesImportantVery ImportantImportantVery ImportantVery ImportantImportantVery ImportantVery ImportantYes
38723.0MaleSingleStudentNo IncomePost Graduate512.898877.5764SnacksDinnerNon Veg foods (Lunch / Dinner)Bakery items (snacks)AgreeNeutralAgreeAgreeAgreeDisagreeAgreeNeutralAgreeNeutralAgreeDisagreeDisagreeNeutralAgreeNeutralNeutralDisagreeStrongly disagreeStrongly disagreeYesWeekend (Sat & Sun)30 minutesAgreeNeutralDisagreeAgreeMaybeSlightly ImportantUnimportantSlightly ImportantModerately ImportantModerately ImportantModerately ImportantModerately ImportantSlightly ImportantYes

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Most frequently occurring

AgeGenderMarital_StatusOccupationMonthly_IncomeEducational_QualificationsFamily_sizelatitudelongitudeMeal(P1)Meal(P2)Perference(P1)Perference(P2)Ease_and_convenientTime_savingMore_restaurant_choicesEasy_Payment_optionMore_Offers_and_DiscountGood_Food_qualityGood_Tracking_systemSelf_CookingHealth_ConcernLate_DeliveryPoor_HygieneBad_past_experienceUnavailabilityUnaffordableLong_delivery_timeDelay_of_delivery_person_getting_assignedDelay_of_delivery_person_picking_up_foodWrong_order_deliveredMissing_itemOrder_placed_by_mistakeInfluence_of_timeOrder_TimeMaximum_wait_timeResidence_in_busy_locationGood_Road_ConditionLow_quantity_low_timeDelivery_person_abilityInfluence_of_ratingLess_Delivery_timeHigh_Quality_of_packageNumber_of_callsPolitenessFreshnessTemperatureGood_TasteGood_QuantityorderAgain# duplicates
2223.0FemaleSingleStudentNo IncomePost Graduate413.048777.5923DinnerDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)Strongly agreeStrongly agreeStrongly agreeNeutralNeutralStrongly agreeStrongly agreeNeutralDisagreeNeutralStrongly agreeAgreeNeutralDisagreeAgreeAgreeAgreeStrongly agreeAgreeNeutralYesWeekend (Sat & Sun)45 minutesNeutralNeutralNeutralNeutralYesImportantImportantModerately ImportantModerately ImportantVery ImportantVery ImportantVery ImportantVery ImportantYes6
1723.0FemaleSingleStudentNo IncomeGraduate513.020677.6479LunchSnacksVeg foods (Breakfast / Lunch / Dinner)Ice cream / Cool drinksStrongly agreeAgreeAgreeNeutralAgreeNeutralNeutralAgreeNeutralAgreeAgreeNeutralAgreeDisagreeNeutralNeutralNeutralNeutralNeutralNeutralYesWeekdays (Mon-Fri)45 minutesNeutralNeutralNeutralNeutralYesSlightly ImportantModerately ImportantSlightly ImportantSlightly ImportantImportantImportantImportantImportantYes4
121.0MaleSingleStudentNo IncomeGraduate213.001277.5995DinnerDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)DisagreeDisagreeNeutralDisagreeNeutralDisagreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeAgreeDisagreeNeutralAgreeDisagreeDisagreeDisagreeYesAnytime (Mon-Sun)30 minutesAgreeAgreeAgreeStrongly AgreeYesModerately ImportantImportantImportantImportantImportantVery ImportantVery ImportantVery ImportantNo3
421.0MaleSingleStudentNo IncomeGraduate512.978377.6408LunchSnacksNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeStrongly agreeAgreeAgreeStrongly agreeAgreeNeutralAgreeNeutralAgreeAgreeDisagreeDisagreeDisagreeDisagreeNeutralDisagreeAgreeAgreeStrongly agreeYesWeekend (Sat & Sun)45 minutesAgreeAgreeStrongly AgreeStrongly AgreeYesImportantImportantVery ImportantImportantSlightly ImportantModerately ImportantImportantImportantYes3
522.0FemaleSingleEmployee25001 to 50000Graduate412.884577.6036LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)Strongly agreeAgreeNeutralNeutralAgreeNeutralAgreeAgreeStrongly agreeNeutralStrongly agreeAgreeAgreeDisagreeDisagreeDisagreeDisagreeAgreeAgreeAgreeYesAnytime (Mon-Sun)45 minutesStrongly AgreeStrongly AgreeAgreeAgreeYesImportantVery ImportantVery ImportantVery ImportantVery ImportantImportantVery ImportantImportantYes3
722.0FemaleSingleStudentNo IncomeGraduate212.936977.6407BreakfastSnacksNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeAgreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeDisagreeDisagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeStrongly disagreeNoAnytime (Mon-Sun)More than 60 minutesAgreeStrongly AgreeAgreeDisagreeMaybeImportantImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantVery ImportantYes3
922.0FemaleSingleStudentNo IncomePost Graduate512.985077.5533SnacksDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeAgreeAgreeAgreeNeutralAgreeAgreeStrongly agreeAgreeStrongly agreeAgreeAgreeAgreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeYesAnytime (Mon-Sun)45 minutesAgreeStrongly AgreeNeutralAgreeYesImportantVery ImportantImportantImportantVery ImportantVery ImportantVery ImportantVery ImportantYes3
1122.0MaleSingleStudentBelow Rs.10000Post Graduate412.985077.5533LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeAgreeAgreeAgreeAgreeNeutralNeutralAgreeStrongly agreeStrongly agreeAgreeStrongly agreeAgreeDisagreeStrongly agreeStrongly agreeNeutralNeutralNeutralDisagreeYesWeekend (Sat & Sun)30 minutesAgreeAgreeAgreeAgreeYesImportantImportantModerately ImportantImportantImportantImportantVery ImportantVery ImportantYes3
1222.0MaleSingleStudentNo IncomeGraduate312.977077.5773LunchDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)Strongly agreeStrongly agreeAgreeStrongly agreeAgreeNeutralAgreeDisagreeAgreeDisagreeAgreeDisagreeStrongly agreeStrongly disagreeNeutralDisagreeDisagreeStrongly agreeStrongly agreeStrongly agreeNoAnytime (Mon-Sun)45 minutesAgreeDisagreeAgreeStrongly AgreeYesVery ImportantVery ImportantUnimportantImportantVery ImportantVery ImportantVery ImportantVery ImportantYes3
1322.0MaleSingleStudentNo IncomeGraduate313.015877.5390SnacksDinnerNon Veg foods (Lunch / Dinner)Veg foods (Breakfast / Lunch / Dinner)AgreeStrongly agreeAgreeNeutralNeutralAgreeStrongly agreeAgreeStrongly agreeAgreeAgreeStrongly agreeAgreeStrongly agreeDisagreeDisagreeNeutralDisagreeDisagreeDisagreeYesAnytime (Mon-Sun)30 minutesAgreeAgreeNeutralAgreeYesImportantVery ImportantImportantVery ImportantImportantVery ImportantVery ImportantImportantYes3